You are already using AI. Most UK manufacturers are. It stalls because the data is not connected across sites, so none of it can see the whole operation. That disconnect is where projects stall and margin quietly leaks.
Siemens runs 600+ data projects on one governed cloud platform, provisioning new projects in minutes rather than weeks. (Siemens)
The board asked for AI and the budget was there. Predictive maintenance, demand forecasting, quality prediction: the use cases are clear. Yet so many get stuck in pilot and never reach the factory floor. The answer is rarely the model. Most pilots work in the lab. They stall when they meet the real operational data, because that data is not standardised, not governed and not connected across sites.
For a CIO or Head of Data, that is the uncomfortable truth behind a stalled programme. The problem is not the AI. It is that the factory cannot see itself.
Because the operational data it needs is fragmented and defined differently everywhere it lives.
A model trained on one site's data cannot generalise to the next, because the next site describes its assets differently. So every pilot becomes a bespoke build. It proves the concept, then collapses under the cost of repeating it across the estate. The model was never the hard part. Connecting and governing the data underneath it was.
Enough to justify connecting it first. Take predictive maintenance as one worked example. Once the data is joined across sites, Deloitte puts the typical gains at a 10 to 20 per cent increase in equipment uptime and a 5 to 10 per cent cut in maintenance costs, with the time spent planning maintenance falling by 20 to 50 per cent.
The headline ranges are the sensible planning numbers. The case example shows what is possible once the data is connected and the models run in production rather than in a pilot. On a capital-intensive asset base, moving uptime by even a few points is money that goes straight to output and margin. And maintenance is only the start: the same connected foundation is what lets demand forecasting and quality prediction see across the operation too.
Because it keeps the first step small, and the foundation you build is reusable. Pick something bounded and measurable that sits on data you already hold. Maintenance is a good candidate, because downtime and maintenance cost are already tracked, so the outcome is easy to value. The connected, governed data model you build to make it work is the same foundation every other use case depends on. Prove it once, and the second and third use case have somewhere to stand.
It is less exotic than it sounds. In practice it means:
This is where a modern data platform earns its place. It lets you bring historian, CMMS and ERP data together, govern it centrally and give teams access without standing up new infrastructure for each project. The right tools depend on your environment, not a vendor preference. We build this across the modern data stack and choose what fits the data you already hold and the skills in your team. For a maintenance use case, that might be Fivetran or Talend to connect historian, CMMS and ERP data, Snowflake or Databricks to standardise and govern it with dbt and DataOps.Live, and Qlik AutoML to build the model. Siemens runs more than 600 data projects across its business divisions on one governed cloud data platform, with teams able to provision new data projects in minutes rather than weeks.
Your AI is not the problem. The factory not being able to see across itself is. Connect the data first, prove it on one use case, and the rest of the roadmap finally has ground to stand on.
The Connected Factory Benchmark is ten questions about your own estate. You get a score out of ten and a sense of how you compare. Or read the full report this foundation sits inside.